AWS Announces Six New Amazon SageMaker Capabilities

Amazon SageMaker Canvas expands access to machine learning by providing business analysts the ability to generate more accurate machine learning predictions using a point-and-click interface—no coding required

Amazon SageMaker Ground Truth Plus offers a fully managed data labeling service that uses a highly skilled workforce and built-in workflows to deliver high-quality annotated data for training machine learning models faster at lower cost

Amazon SageMaker Studio now makes data engineering, analytics, and machine learning workflows accessible within a universal notebook

Amazon SageMaker Training Compiler helps customers train deep learning models up to 50% faster by automatically compiling code to make it more efficient

Amazon SageMaker Inference Recommender automatically suggests the optimal AWS compute instances for running machine learning inference with the best price performance

Amazon SageMaker Serverless Inference offers serverless compute for machine learning inference at scale

Today, at AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ:AMZN), announced six new capabilities for its industry-leading machine learning service, Amazon SageMaker, that make machine learning even more accessible and cost effective. Today’s announcements bring together powerful new capabilities, including a no-code environment for creating accurate machine learning predictions, more accurate data labeling using highly skilled annotators, a universal Amazon SageMaker Studio notebook experience for greater collaboration across domains, a compiler for machine learning training that makes code more efficient, automatic compute instance selection machine learning inference, and serverless compute for machine learning inference. To get started with Amazon SageMaker, visit aws.amazon.com/sagemaker.

Driven by the availability of virtually infinite compute capacity, a massive proliferation of data in the cloud, and the rapid advancement of the tools available to developers, machine learning has become mainstream across many industries. For years, AWS has focused on making machine learning more accessible to a broader audience of customers. Today, Amazon SageMaker is one of the fastest growing services in AWS history with tens of thousands of customers, including AstraZeneca, Aurora, Capitol One, Cerner, Discovery, Hyundai, Intuit, Thomson Reuters, Tyson, Vanguard, and many more customers who use the service to train machine learning models of all sizes, some of which on the extreme now consist of billions of parameters capable of making hundreds of billions of predictions every month. As customers further scale their machine learning model training and inference on Amazon SageMaker, AWS has continued to invest in expanding the service’s capability, delivering more than 60 new Amazon SageMaker features and functionalities in the past year alone. Today’s announcements build on these advancements to make it even easier to prepare and gather data for machine learning, train models faster, optimize the type and amount of compute needed for inference, and expand machine learning to an even broader audience.

• Amazon SageMaker Canvas no-code machine learning predictions: Amazon SageMaker Canvas expands access to machine learning by providing business analysts (line-of-business employees supporting finance, marketing, operations, and human resources teams) with a visual interface that allows them to create more accurate machine learning predictions on their own—without requiring any machine learning experience or having to write a single line of code. As more companies seek to reinvent their businesses and customer experiences with machine learning, more people in their organizations need to be able to use advanced machine learning technology across different lines of business. However, machine learning has typically required specialized skills that can require years of formal education or intensive training with a challenging and evolving curriculum. Amazon SageMaker Canvas solves this challenge by providing a visual, point-and-click user interface that makes it easy for business analysts to generate predictions. Customers point Amazon SageMaker Canvas to their data stores (e.g. Amazon Redshift, Amazon S3, Snowflake, on-premises data stores, local files, etc.), and the Amazon SageMaker Canvas provides visual tools to help users intuitively prepare and analyze data. Amazon SageMaker Canvas then uses automated machine learning to build and train machine learning models without any coding. Business analysts can review and evaluate models in the Amazon SageMaker Canvas console for accuracy and efficacy for their use case. Amazon SageMaker Canvas also lets users export their models to Amazon SageMaker Studio, so they can share them with data scientists to validate and further refine their models.
• Amazon SageMaker Ground Truth Plus expert data labeling: Amazon SageMaker Ground Truth Plus is a fully managed data labeling service that uses an expert workforce with built-in annotation workflows to deliver high-quality data for training machine learning models faster and at lower cost with no coding required. Customers need increasingly larger datasets that are correctly labeled to train ever more accurate models and scale their machine learning deployments. However, producing large datasets can take anywhere from weeks to years and often requires companies to hire a workforce and create workflows to manage the process of labeling data. In 2018, AWS launched Amazon SageMaker Ground Truth to make it easier for customers to produce labeled data using human annotators through Amazon Mechanical Turk, third-party vendors, or their own private workforce. Amazon SageMaker Ground Truth Plus expands on this capability with a specialized workforce with specific domain and industry expertise, as well as qualifications to meet customers’ data security, privacy, and compliance requirements for highly accurate data labeling. Amazon SageMaker Ground Truth Plus has a multistep labeling workflow that includes pre-labeling powered by machine learning models, machine validation of human labeling to detect errors and low-quality labels, and assistive labeling features (e.g. 3D cuboid snapping, removal of distortion in 2D images, predict-next in video labeling, and auto-segment tools) to reduce the time required to label datasets and help reduce the cost of procuring high-quality annotated data. To get started, customers simply point Amazon SageMaker Ground Truth Plus to their data source in Amazon Simple Storage Service (Amazon S3) and provide their specific labeling requirements (e.g. instructions for how medical experts should label anomalies in radiology images of lungs). Amazon SageMaker Ground Truth Plus then creates a data labeling workflow and provides dashboards that allow customers to follow data annotation progress, inspect samples of completed labels for quality, and provide feedback to generate high-quality data so customers can build, train, and deploy highly accurate machine learning models more quickly.
• Amazon SageMaker Studio universal notebooks: A universal notebook for Amazon SageMaker Studio (the first complete IDE for machine learning) provides a single, integrated environment to perform data engineering, analytics, and machine learning. Today, teams across different data domains want to collaborate using a range of data engineering, analytics, and machine learning workflows. The practitioners of these domains often cross areas of knowledge like data engineering, data analytics, and data science and want to be able to work across the various workflows without needing to switch data exploration tools. However, when customers are ready to integrate data across analytics and machine learning environments, they often have to juggle multiple tools and notebooks, which can be cumbersome, time consuming, and prone to error. Amazon SageMaker Studio now allows users to interactively access, transform, and analyze a wide range of data for multiple purposes all from within a universal notebook. With built-in integration with Spark, Hive, and Presto running on Amazon EMR clusters and data lakes running on Amazon S3, customers can now use Amazon SageMaker Studio to access and manipulate data in a universal notebook without having to switch services. In addition to developing machine learning models using their preferred framework (e.g. TensorFlow, PyTorch, or MXNet) to build, train, and deploy machine learning models in Amazon SageMaker Studio, customers can browse and query data sources, explore metadata and schemas, and start processing jobs for analytics or machine learning workflows—without leaving the universal Amazon SageMaker Studio notebook.
• Amazon SageMaker Training Compiler for machine learning models: Amazon SageMaker Training Compiler is a new machine learning model compiler that automatically optimizes code to use compute resources more effectively and reduce the time it takes to train models by up to 50%. Today’s state-of-the-art deep learning models are so large and complex that they require specialized compute instances to accelerate training and can consume thousands of hours of graphical processing unit (GPU) compute time to train a single model. To further accelerate training times, data scientists typically try to augment training data or tune hyperparameters (variables that govern the machine learning training process) to find the best performing and least resource-intensive version of a model. This work is technically complicated, and data scientists often do not have time to optimize the frameworks needed to train models to run on GPUs. Amazon SageMaker Training Compiler is a new machine learning model compiler that is integrated with the versions of TensorFlow and PyTorch in Amazon SageMaker that have been optimized to run more efficiently in the cloud, so data scientists can use their preferred frameworks to train machine learning models through more efficient use of GPUs. With a single click, Amazon SageMaker Training Compiler automatically optimizes the trained model and compiles it to execute training up to 50% faster.
• Amazon SageMaker Inference Recommender automatic instance selection: Amazon SageMaker Inference Recommender helps customers automatically select the best compute instance and configuration (e.g. instance count, container parameters, and model optimizations) to power a particular machine learning model. For large machine learning models commonly used for natural language processing or computer vision, selecting a compute instance with the best price performance is a complicated, iterative process that can take weeks of experimentation. Amazon SageMaker Inference Recommender removes the guesswork and complexity of determining where to run a model and can reduce the time to deploy from weeks to hours by automatically recommending the ideal compute instance configuration. Data scientists can use Amazon SageMaker Inference Recommender to deploy the model to one of the recommended compute instances, or they can use the service to run a performance benchmark simulation across a range of selected compute instances. Customers can review benchmark results in Amazon SageMaker Studio and evaluate the tradeoffs between different configuration settings including latency, throughput, cost, compute, and memory.
• Amazon SageMaker Serverless Inference for machine learning models: Amazon SageMaker Serverless Inference offers pay-as-you-go pricing inference for machine learning models deployed in production. Customers are always looking to optimize costs when using machine learning, and this becomes increasingly important for applications that have intermittent traffic patterns with long idle times. For example, applications like personalized recommendations based on consumer purchase patterns, chatbots fielding incoming customer calls, and forecasting demand based on real-time transactions can have peaks of activity based on external factors like weather conditions, promotional offerings, or holidays. Providing just the right amount of compute for machine learning inference is a difficult balancing act. In some cases, customers over-provision capacity to accommodate peak activity, which allows for consistent performance but wastes money when there is no traffic. In other cases, customers under-provision compute to constrain costs at the expense of providing enough compute capacity to perform inference when conditions change. Some customers try manually adjusting computing resources on the fly to accommodate changing conditions, but this is tedious and manual work. Amazon SageMaker Serverless Inference for machine learning automatically provisions, scales, and turns off compute capacity based on the number of inference requests. When customers deploy their machine learning model into production, they simply select the serverless deployment option in Amazon SageMaker, and Amazon SageMaker Serverless Inference manages compute resources to provide the precise amount of compute needed. With Amazon SageMaker Serverless Inference, customers only pay for the compute capacity they use for each request and the amount of data processed, without having to manage the underlying infrastructure.

“Customers across all industries and sizes are excited about how Amazon SageMaker has helped them scale their use of machine learning such that it has become a core part of their operations and allows them to invent new products, services, and experiences for the world,” said Bratin Saha, Vice President of Amazon Machine Learning at AWS. “We’re excited to expand our industry-leading machine learning service to an even broader group of customers, so they too can drive innovation in their business and help solve challenging problems. With these new Amazon SageMaker tools, we’re introducing a whole new group of users to the service while also providing additional capabilities for existing customers to make it easier to transform data into valuable insights, accelerate time to deployment, improve performance, and save money throughout the machine learning journey.”

The BMW Group, headquartered in Munich, Germany, is a global manufacturer of premium automobiles and motorcycles, covering the brands BMW, BMW Motorrad, MINI, and Rolls-Royce. It also provides premium financial and mobility services. “The use of artificial intelligence as a key technology is an integral element in the process of digital transformation at the BMW Group. The company already employs AI throughout the value chain, enabling it to generate added value for customers, products, employees, and processes. In the past few years, we have industrialized many top BMW Group use cases, measured by business value impact,” said Marc Neumann, Product Owner, AI Platform at The BMW Group. “We believe Amazon SageMaker Canvas can add a boost to our AI/ML scaling across the BMW Group. With SageMaker Canvas, our business users can easily explore and build ML models to make accurate predictions without writing any code. SageMaker also allows our central data science team to collaborate and evaluate the models created by business users before publishing them to production.”

Siemens Energy is energizing society. They are transforming in key focus areas of environmental, social, and governance (ESG) and their innovation is making the future of tomorrow different today, for both their partners—and their people. “The core of our data science strategy at Siemens Energy is to bring the power of machine learning to all business users by enabling them to experiment with different data sources and machine learning frameworks without requiring a data science expert. This enables us to increase the speed of innovation and digitalization of our energy solutions such as Dispatch Optimizer and Diagnostic services,” said Davood Naderi, Data Science Team Lead at Industrial Applications for Siemens Energy. “We found Amazon SageMaker Canvas a great addition to the Siemens Energy machine learning toolkit, because it allows business users to perform experiments while also sharing and collaborating with data science teams. The collaboration is important because it helps us productionalize more ML models and ensure all models adhere to our quality standards and policies.”

Airbnb is one of the world’s largest marketplaces for unique, authentic places to stay and things to do, offering over 7 million accommodations and 40,000 handcrafted activities, all powered by local hosts. “At Airbnb, we are increasingly integrating ML across all aspects of our business. As a result, our teams consistently need to generate and maintain high-quality data in order to train and test ML models,” said Wei Luo, Data Scientist at Airbnb China. “We were looking for a way to generate high quality text classification data results on one hundred thousand paragraphs of customer service logs in Mandarin so we can better serve our customers and reduce dependencies on our customer service team. With Amazon SageMaker Ground Truth Plus, the AWS team built a customized data labeling workflow, which included a customized ML model that was able to achieve 99% classification accuracy.”

The National Football League is America’s most popular sports league, comprised of 32 franchises that compete each year to win the Super Bowl, the world’s biggest annual sporting event. “At the NFL, we continue to look for new ways to use machine learning to help our fans, broadcasters, coaches, and teams benefit from deeper insights,” said Jennifer Langton, SVP, Player Health and Innovation at NFL. “Football is a fast moving sport where plays can happen in a split second. While coaches and referees carefully watch the game, it can be difficult to watch all players on a field for safety. Computer vision allows us to accurately detect player safety incidents, but developing these algorithms requires expertly labeled data. Now with Amazon SageMaker Ground Truth Plus, we have custom workflows and user interfaces for sophisticated labeling tasks, which helps us improve player safety.”

Founded and headquartered in Orange County, California, VIZIO’s mission is to deliver immersive entertainment and compelling lifestyle enhancements that make its products the center of the connected home. VIZIO is driving the future of televisions through its integrated platform of cutting-edge Smart TVs and powerful SmartCast operating system. VIZIO’s platform gives content providers more ways to distribute their content and advertisers more tools to target and dynamically serve ads to a growing audience that is increasingly transitioning away from linear TV. “At VIZIO, we consistently look for ways to leverage ML to create personalized experiences for our customers. We were looking for a way to continuously review ad videos and generate commercial metadata for efficient ads classification,” said Zeev Neumeier, Chief Innovation Officer at VIZIO. “With the use of Amazon SageMaker Ground Truth Plus’s streaming capability, we can now use a custom template which provides video classification, metadata collection, and an automated system that enables data collection in real time as ads air. With Amazon SageMaker Ground Truth Plus, we are able to review the results in less than one business day.”

Litterati is a data science company empowering people to ‘crowdsource-clean’ the planet. Litterati’s platform empowers people to create better solutions for the litter and waste problems our world faces by developing behavioral insight, mapping problem areas, and mitigating future risk. From schools to scientists, environmental organizations, brands, and city governments, people are coming together using Litterati for the greater good to create a litter-free world. “For us, machine learning brings light to unseen challenges. In the US alone, each year billions of dollars are spent cleaning up litter,” said Sean Doherty, CTO at Litterati. “With computer vision models, we transform images of litter all around the world into data, so cities can better allocate their litter management resources. However, building object detection models requires access to object, material, and brand information, as well as localized knowledge due to datasets being spread across the globe. Amazon SageMaker Ground Truth Plus allows us to create a hierarchical annotation interface that captures these precise features within that localized context. In addition, the SageMaker Ground Truth Plus expert workforce created localized image annotations, which provides a standardized solution increasing our data labeling efficiency by up to 20%, accelerating our ability to ingest annotated results into our database by 200%, and reducing post-processing time by 90%.”

Provectus helps its customers build end-to-end data and machine learning engineering experiences from raw datasets, enterprise data lakes, and machine learning models. “We have been waiting for a feature to create and manage Amazon EMR clusters directly from Amazon SageMaker Studio so that our customers could run Spark, Hive, and Presto workflows directly from Amazon SageMaker Studio notebooks,” said Stepan Pushkarev, CEO at Provectus. “We are excited that Amazon SageMaker has now natively built this capability to simplify management of Spark and machine learning jobs. This will help our customers’ data engineers and data scientists collaborate more effectively to perform interactive data analysis and develop machine learning pipelines with EMR-based data transformations.”

The Vanguard Group, Inc., is an American registered investment advisor based in Malvern, Pennsylvania, with about $7 trillion in global assets under management. Vanguard is redefining the industry by doing what’s right for investors and creating change for millions of clients worldwide. “We’re excited that our Vanguard data scientists and data engineers can now collaborate in a single notebook for analytics and machine learning,” said Doug Stewart, Senior Director of Data and Analytics at Vanguard. “Now that Amazon SageMaker Studio has built-in integrations with Spark, Hive, and Presto all running on Amazon EMR, our development teams can be more productive. This single development environment will allow our teams to focus on building, training, and deploying machine learning models.”

Quantum Health is on a mission to make healthcare navigation smarter, simpler, and more cost-effective for everyone. They use Amazon SageMaker for use cases like text classification, text summarization, predictive models, classification problems, and Q&A to help the Quantum team and the members they serve. “Iterating with NLP models can be a challenge because of their size. Long training times bog down workflows and high costs can discourage our team from trying larger models that might offer better performance,” said Jorge Lopez Grisman, Senior Data Scientist at Quantum Health. “Amazon SageMaker Training Compiler is exciting because it has the potential to alleviate these frictions. Achieving a speedup with Amazon SageMaker Training Compiler is a real win for our team that will make us more agile and innovative moving forward.”

Guidewire is the platform property and casualty insurers trust to engage, innovate, and grow efficiently. The company combines digital, core, analytics, and AI to deliver its platform as a cloud service, and it enables its customers to do advanced analytics and machine learning for their industry-specific workloads. More than 450 insurers, from new ventures to the largest and most complex in the world run on Guidewire. “One of Guidewire’s services is to help customers develop cutting-edge NLP models for applications like risk assessment and claims operations. Amazon SageMaker Training Compiler is compelling because it offers time and cost savings to our customers while developing these NLP models,” said Matt Pearson, Principal Product Manager, Analytics and Data Services at Guidewire Software. “We expect it to help us reduce training time by more than 20% through more efficient use of GPU resources. We are excited to implement Amazon SageMaker Training Compiler in our NLP workloads, helping us to accelerate the transformation of data to insight for our customers.”

Musixmatch is a leading music data company providing data, tools, and services that enrich the way we experience music such as searching for songs and sharing song lyrics. Musixmatch is the largest service of this kind in the world with over 80 million users and over 8 million distinct lyrics. “Musixmatch uses Amazon SageMaker to build natural language processing and audio processing models, and is experimenting using Hugging Face with Amazon SageMaker. We choose Amazon SageMaker because it allows data scientists to iteratively build, train, and tune models quickly without having to worry about managing the underlying infrastructure, which means data scientists can work more quickly and independently,” said Loreto Parisi, AI Engineering Director at Musixmatch. “As the company has grown, so too have our requirements to train and tune larger and more complex NLP models. We are always looking for ways to accelerate training time while also lowering training costs which is why we are excited about Amazon SageMaker Training Compiler. SageMaker Training Compiler provides more efficient ways to use GPUs during the training process and, with the seamless integration between SageMaker Training Compiler, PyTorch, and high-level libraries like Hugging Face, we have seen a significant improvement in training time of our transformer-based models going from weeks to days as well as lower training costs.”

Loka, a machine learning consulting firm, helps its clients harness and build ML into their products across a wide range of use cases to deliver better customer experiences. “We spend a lot of time and effort optimizing models, tuning servers, and testing instance types to deliver performant, scalable, and cost effective ML environments for its client,” said Bobby Mukherjee, CEO at Loka. “Now using Amazon SageMaker Inference Recommender, our engineers are able to get an ML model deployed to production within minutes from any location.”

Holmusk, a digital health company, launched its FoodDX app to help people improve their diet and health. “Our food image recognition algorithms need low latency to ensure our users get the right diet recommendations at the right time. To achieve low latency, we were over-provisioning GPUs, which was expensive,” said Sai Subramanian, CTO at Holmusk. “Using Amazon SageMaker Inference Recommender, we can now easily conduct load tests across different instances and determine an instance configuration within hours to reduce our compute costs significantly while maintaining latency requirements. This is a huge win for our team and lets our ML scientists focus on creating algorithms to help people live healthier lives rather than managing infrastructure.”

Qualtrics is an experience management company that helps extract information from customer surveys using natural language processing (NLP) models. “Amazon SageMaker Inference Recommender improves the efficiency of our MLOps teams with the tools required to test and deploy machine learning models at scale,” saidSamir Joshi, ML Engineer at Qualtrics. “With Amazon SageMaker Inference Recommender, our team can define latency and throughput requirements and quickly deploy these models faster, while also meeting our budget and production criteria.”

iFood, a leading player in online food delivery in Latin America fulfilling over 60 million orders each month, uses machine learning to make restaurant recommendations to its customers ordering online. “We have been using Amazon SageMaker for our machine learning models to build high-quality applications throughout our business,” said Ivan Lima, Director of Machine Learning and Data Engineering at iFood. “With Amazon SageMaker Serverless Inference, we expect to be able to deploy even faster and scale models without having to worry about selecting instances or keeping the endpoint active when there is no traffic. With this, we also expect to see a cost reduction to run these services.”

About Amazon Web Services

For over 15 years, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud offering. AWS has been continually expanding its services to support virtually any cloud workload, and it now has more than 200 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, virtual and augmented reality (VR and AR), media, and application development, deployment, and management from 81 Availability Zones within 25 geographic regions, with announced plans for 27 more Availability Zones and nine more AWS Regions in Australia, Canada, India, Indonesia, Israel, New Zealand, Spain, Switzerland, and the United Arab Emirates. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com.

About Amazon

Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Amazon strives to be Earth’s Most Customer-Centric Company, Earth’s Best Employer, and Earth’s Safest Place to Work. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Career Choice, Fire tablets, Fire TV, Amazon Echo, Alexa, Just Walk Out technology, Amazon Studios, and The Climate Pledge are some of the things pioneered by Amazon. For more information, visit amazon.com/about and follow @AmazonNews.

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AWS Announces Two New Initiatives That Make Machine Learning More Accessible

New $10 million AWS Artificial Intelligence and Machine Learning Scholarship (AWS AI & ML Scholarship) program is designed to prepare underrepresented and underserved students globally for careers in machine learning

Amazon SageMaker Studio Lab makes it easy for anyone to quickly set up a machine learning development environment for learning and experimentation at no cost

Today, at AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ:AMZN), announced two new initiatives designed to make machine learning more accessible for anyone interested in learning and experimenting with the technology. The AWS AI & ML Scholarshipis a new education and scholarship program aimed at preparing underrepresented and underserved students globally for careers in machine learning. The program uses AWS DeepRacer and the new AWS DeepRacer Student League to teach students foundational machine learning concepts by giving them hands-on experience training machine learning models for autonomous race cars, while providing educational content centered on machine learning fundamentals. AWS is further increasing access to machine learning through Amazon SageMaker Studio Lab, which gives everyone access to a no-cost version of Amazon SageMaker—an AWS service that helps customers build, train, and deploy machine learning models.

“The two initiatives we are announcing today are designed to open up educational opportunities in machine learning to make it more widely accessible to anyone who is interested in the technology,” said Swami Sivasubramanian, Vice President of Amazon Machine Learning at AWS. “Machine learning will be one of the most transformational technologies of this generation. If we are going to unlock the full potential of this technology to tackle some of the world’s most challenging problems, we need the best minds entering the field from all backgrounds and walks of life. We want to inspire and excite a diverse future workforce through this new scholarship program and break down the cost barriers that prevent many from getting started with machine learning.”

New $10 million education and scholarship program is designed to prepare underrepresented and underserved students globally for careers in machine learning

The World Economic Forum estimates that technological advances and automation will create 97 million new technology jobs by 2025, including in the field of artificial intelligence and machine learning. While the job opportunities in technology are growing, diversity is lagging behind in science and technology careers. Making educational resources available to anyone interested in technology is critical to encouraging a more robust, diverse pipeline of people in artificial intelligence and machine learning careers. The new AWS AI & ML Scholarship aims to help underrepresented and underserved high school and college students learn foundational machine learning concepts and prepare them for careers in artificial intelligence and machine learning. In addition to no-cost access to dozens of hours of free machine learning model training and educational materials, 2,000 qualifying students from underrepresented and underserved communities will win a scholarship for the AI Programming with Python Udacity Nanodegree program, designed to give scholarship recipients the programming tools and techniques fundamental to machine learning. Graduates from the first Nanodegree program will be invited to take a technical assessment. Five hundred students who receive the highest scores in this assessment will earn a second Udacity Nanodegree program scholarship on deep learning and machine learning engineering to help further prepare them for a career in artificial intelligence and machine learning. These top 500 students will also have access to mentorship opportunities from tenured Amazon and Intel technology experts for career insights and advice.

Delivered in collaboration with Intel and supported by the talent transformation platform Udacity, the AWS AI & ML Scholarship program allows students from around the world to access dozens of hours of free training modules and tutorials on the basics of machine learning and its real-world applications. Students can use AWS DeepRacer to turn theory into hands-on action by learning how to train machine learning models to power a virtual race car. Students who successfully complete educational modules by passing knowledge-check quizzes, meet certain AWS DeepRacer lap time performance targets, and submit an essay will be considered for Udacity Nanodegree program scholarships. Students can also put their virtual race cars to the test in the new AWS DeepRacer Student League. The AWS DeepRacer Student League helps people of all skill levels learn how to build machine learning models with a fully autonomous 1/18th scale race car driven by machine learning, a 3D racing simulator, and a global competition. AWS DeepRacer has been used by enterprises like Capital One, BMW, Deloitte, JP Morgan Chase, Accenture, and Liberty Mutual to teach their employees to build, train, and deploy machine learning models in a hands-on way. To get started with the AWS AI & ML Scholarship, visit awsaimlscholarship.com.

Amazon SageMaker Studio Lab provides no-cost access to a machine learning development environment to put machine learning in the hands of everyone

Amazon SageMaker Studio Lab offers a free version of Amazon SageMaker, which is used by researchers and data scientists worldwide to build, train, and deploy machine learning models quickly. Amazon SageMaker Studio Lab removes the need to have an AWS account or provide billing details to get up and running with machine learning on AWS. Users simply sign up with an email address through a web browser, and Amazon SageMaker Studio Lab provides access to a machine learning development environment. Amazon SageMaker Studio Lab provides unlimited user sessions that include 15 gigabytes of persistent storage to store projects and up to 12 hours of CPU and four hours of GPU compute for training machine learning models at no cost. There are no cloud resources to build, scale, or manage with Amazon SageMaker Studio Lab, so users can start, stop, and restart working on machine learning projects as easily as closing and opening a laptop. When users are done experimenting and want to take their ideas to production, they can easily export their machine learning projects to Amazon SageMaker Studio to deploy and scale their models on AWS. Amazon SageMaker Studio Lab can be used as a no-cost learning environment for students or a no-cost prototyping environment for data scientists where everyone can quickly and easily start building and training machine learning models with no financial obligation or long-term commitments. To learn more about Amazon SageMaker Studio Lab, visit aws.amazon.com/sagemaker/studio-lab.

Earlier this year, Amazon announced a new Leadership Principle: Success and Scale Bring Broad Responsibility. AWS is scaling and investing in initiatives to live up to this new Leadership Principle, including Amazon’s commitment to provide 29 million people with access to free cloud computing skills training by 2025, science, technology, engineering, and math (STEM) education programs for young learners including Amazon Future Engineer, AWS Girls’ Tech Day, and AWS GetIT, as well as collaborations with colleges and universities. Now, AWS is making it easier for more people from underrepresented groups and underserved populations to get started with machine learning—with free education, scholarships, and access to the same machine learning technology used by the world’s leading startups, research institutions, and enterprises. The two initiatives announced today further advance Amazon’s efforts to make education and training opportunities widely accessible.

AWS and Intel have a 15-year relationship dedicated to developing, building, and supporting cloud services that are designed to manage cost and complexity, accelerate business outcomes, and scale to meet current and future computing requirements. “As an industry, we must do more to create a diverse and inclusive tech workforce,” said Michelle Johnston Holthaus, Executive Vice President and GM of the Sales, Marketing, and Communications Group at Intel. “Intel is proud to support initiatives like the AWS AI & ML Scholarship program, which aligns with our commitment to provide more access to STEM opportunities for underrepresented groups and helps diversify the future generation of machine learning practitioners. What makes this education and scholarship program unique is that students are given access to a rich set of learning materials at the outset. This is critical to really move the needle. Learning isn’t contingent on winning but instead part of the process.”

Girls in Tech is a global nonprofit organization dedicated to eliminating the gender gap in tech. “Driving diversity in machine learning requires intentional programs that create opportunities and break down barriers like the new AWS AI & ML Scholarship program,” said Adriana Gascoigne, Founder and CEO of Girls in Tech. “Progress in bringing more women and underrepresented communities into the field of machine learning will only be achieved if everyone works together to close the diversity gap. Girls in Tech is glad to see multi-faceted programs like the AWS AI & ML Scholarship to help close the gap in machine learning education and open career potential among these groups.”

Hugging Face is an AI community for building, training, and deploying state of the art models powered by the reference open source in machine learning. “At Hugging Face, our mission is to democratize state of the art machine learning,” said Jeff Boudier, Director of Product Marketing at Hugging Face. “With Amazon SageMaker Studio Lab, AWS is doing just that by enabling anyone to learn and experiment with ML through a web browser, without the need for a high-powered PC or a credit card to get started. This makes ML more accessible and easier to share with the community. We are excited to be part of this launch and contribute Hugging Face transformers examples and resources to make ML even more accessible!”

Santa Clara University’s mission with the Department of Finance is to educate students, at the undergraduate and graduate levels, to serve their organizations and society in the Jesuit tradition. “Amazon SageMaker Studio Lab will help my students learn the building blocks of machine learning by removing the cloud configuration steps required to get started. Now, in my natural language processing classes, students have more time to enhance their skills,” said Sanjiv Das, Professor of Finance and Data Science at Santa Clara University. “Amazon SageMaker Studio Lab enables students to onboard to AWS quickly, work and experiment for a few hours, and easily pick up where they left off. Amazon SageMaker Studio Lab brings the ease of use of Jupyter notebooks in the cloud to both beginner and advanced students studying machine learning.”

University of Pennsylvania Engineering is the birthplace of the modern computer. It was there that ENIAC, the world’s first electronic, large-scale, general-purpose digital computer, was developed in 1946. For over 70 years, the field of computer science at Penn has been marked by exciting innovations. “One of the hardest parts about programming with machine learning is configuring the environment to build. Students usually have to choose the compute instances, security polices, and provide a credit card,” said Dan Roth, Professor of Computer and Information Science at University of Pennsylvania. “My students needed Amazon SageMaker Studio Lab to abstract away all of the complexity of setup and provide a free powerful sandbox to experiment. This lets them write code immediately without needing to spend time configuring the ML environment.”

About Amazon Web Services

For over 15 years, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud offering. AWS has been continually expanding its services to support virtually any cloud workload, and it now has more than 200 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, virtual and augmented reality (VR and AR), media, and application development, deployment, and management from 81 Availability Zones (AZs) within 25 geographic regions, with announced plans for 27 more Availability Zones and nine more AWS Regions in Australia, Canada, India, Indonesia, Israel, New Zealand, Spain, and Switzerland, and the United Arab Emirates. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com.

About Amazon

Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Amazon strives to be Earth’s Most Customer-Centric Company, Earth’s Best Employer, and Earth’s Safest Place to Work. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Career Choice, Fire tablets, Fire TV, Amazon Echo, Alexa, Just Walk Out technology, Amazon Studios, and The Climate Pledge are some of the things pioneered by Amazon. For more information, visit amazon.com/about and follow @AmazonNews.

View source version on businesswire.com: https://www.businesswire.com/news/home/20211201005992/en/

AWS Announces Three New Database Capabilities

Amazon RDS Custom gives customers a managed service for business applications that require database and operating system customization

Amazon DynamoDB Standard-Infrequent Access (Standard-IA) table class reduces DynamoDB costs by up to 60% for tables that store infrequently accessed data

Amazon DevOps Guru for RDS uses machine learning to better detect, diagnose, and resolve hard-to-find database-related performance issues in minutes not days

Mercado Libre, NetApp, and Amazon among customers and partners using new database capabilities

Today, at AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ:AMZN), announced three new database capabilities that make it easier and more cost efficient for customers to scale and run the right databases for their job. Today’s announcements introduce a new managed database service for business applications that allows customers to customize the underlying database and operating system, a new table class for Amazon DynamoDB designed to reduce storage costs for infrequently accessed data, and a service that uses machine learning to better diagnose and remediate database-related performance issues. Together, these innovations make it easier and more cost effective for customers to manage data at scale.

As a growing number of applications need to work with petabytes—or even exabytes—of data with low latency and high performance, a one-size-fits-all database no longer meets the needs of customers who require highly available, reliable, and performant ways to leverage and manage data at scale. To meet these demands, more and more customers are looking to choose the right database for their unique needs. AWS offers the broadest and deepest selection of specialized database engines, including DynamoDB for key-value databases, Amazon Neptune for graph databases, Amazon ElastiCache and Amazon MemoryDB for Redis for in-memory databases, Amazon DocumentDB for document databases, Amazon Keyspaces (for Apache Cassandra) for wide-column databases, Amazon Timestream for time series databases, and Amazon Quantum Ledger Database (Amazon QLDB) for ledger databases. AWS is also the best place to run open-source databases, which is why more than 100,000 customers choose to run their MySQL and PostgreSQL-compatible databases on Amazon Aurora and enjoy the performance and availability of the highest-grade commercial databases at one-tenth the cost. Many customers also choose to run commercial databases on AWS to take advantage of the superior scalability, security, and elasticity AWS provides. Amazon Relational Database Service (Amazon RDS) offers a fully managed relational database service that makes it easy to set up, operate, and scale Oracle and Microsoft SQL Server databases in the cloud. When it comes to databases, AWS offers the right tools for the job, with more than 15 purpose-built database engines that provide customers with high availability, performance, reliability, and security for a wide range of use cases. The three new database capabilities announced today deliver significant new features to give customers even more choice and higher performance at lower costs.

“Customers have told us they want databases that are optimized for their most important use cases to deliver flexible, scalable, and reliable user experiences without worrying about the resource-intensive burden of managing infrastructure or incurring excess costs,” said Raju Gulabani, Vice President of Databases and Analytics at AWS. “With today’s announcements, we’re excited to provide customers with even more flexibility and choice to easily improve database performance, optimize cost, and power their most business-critical applications. No one else in the industry can match the depth of capability and breadth of selection in databases offered by AWS, and we’re nowhere close to being done innovating for our customers.”

Amazon RDS Custom gives customers a managed database service for business applications that require customization of the underlying database and operating system

Customers who want to run commercial databases like Oracle and Microsoft SQL Server in the cloud choose Amazon RDS because it is easy to set up, operate, and scale. With Amazon RDS, customers no longer need to worry about time-consuming administrative tasks like provisioning capacity, scaling, and backing up their data. However, some business applications require customization to their underlying Oracle and Microsoft SQL Server database environment and operating system (e.g. Microsoft Dynamics AX, Microsoft SharePoint, and Oracle PeopleSoft). Today, customers often run these applications in a self-managed environment (e.g. on Amazon EC2 or on-premises) so they can have full control over the underlying database environment and operating system. While self-managed deployments are highly configurable, customers must spend time on administrative tasks like hardware provisioning, database setup, patching, and backups. What customers running applications that require database and operating system customization want instead is to automate these undifferentiated administrative tasks to make it easier to run these applications on AWS.

Amazon RDS Custom automates the setup, operation, and scaling of the Oracle and Microsoft SQL Server databases that are tightly integrated with common business applications, while allowing customization to the database and underlying operating system these applications require. With Amazon RDS Custom, customers running these types of business applications no longer need to worry about time-consuming administrative tasks like provisioning and scaling hardware, database setup, patching, and backups. Customers can use Amazon RDS Custom to configure their database environment and underlying operating system to modify settings, install custom patches, and integrate third-party software to meet the requirements of their business applications (e.g. custom database minor versions, third-party security and diagnostic software, or specific file system configurations). Amazon RDS Custom automatically monitors the database environment and operating system to detect user-initiated configurations that impact the ability of Amazon RDS Custom to manage the database. If an issue is detected, Amazon RDS Custom will attempt to automatically resolve the issue. For configuration errors that cannot be automatically corrected, Amazon RDS Custom notifies the customer that corrective action is required and provides recommended steps for resolution. Customers can easily move their existing self-managed Oracle and Microsoft SQL Server databases that require specialized customizations to Amazon RDS Custom and no longer worry about having to manage databases themselves. To get started with Amazon RDS Custom, visit aws.amazon.com/rds/custom.

Amazon DynamoDB Standard-Infrequent Access (Standard-IA) table class reduces DynamoDB costs by up to 60% for tables that store infrequently accessed data

Customers choose DynamoDB for high-volume NoSQL workloads because it offers high throughput with consistent millisecond response times at virtually any scale without having to manage servers or clusters. As the patterns of DynamoDB workloads have become more diverse, there is a set of customers who have workloads where storage is the dominant cost for data that needs to be accessed less frequently over time but still requires fast response times when needed. For example, older social media posts, less recent ecommerce orders, and past video game achievements might represent a significant storage expense for customers due to their growing volume and the relatively high cost of storing this data, but they still require high throughput because when this data is requested it needs to be made immediately available. Today, customers optimize costs in these cases by writing code to move older, less frequently accessed data from DynamoDB to lower cost storage alternatives like Amazon S3.

With the new Amazon DynamoDB Standard-IA table class, customers can reduce DynamoDB costs by up to 60% for tables that store infrequently accessed data. The DynamoDB Standard-IA table class offers up to 60% lower storage costs than Standard DynamoDB tables, making it the most cost-effective option for tables where storage is the dominant table cost. In contrast, the DynamoDB Standard table class offers up to 20% lower throughput costs than the Standard-IA table class and remains the most cost-effective option for tables where throughput is the dominant table cost. Customers can switch between DynamoDB Standard and DynamoDB Standard-IA table classes with no impact to table performance and no code changes required to optimize their spend for the type of data they are storing. To get started with the DynamoDB Standard-IA table class, visit aws.amazon.com/dynamodb/standard-ia.

Amazon DevOps Guru for RDS uses machine learning to better detect and diagnose hard-to-find database-related performance issues and provides recommendations designed to resolve them in minutes not days

Amazon DevOps Guru is a machine learning powered service that makes it easier for developers to improve application availability by automatically detecting operational issues and recommending specific actions for remediation. Today, Amazon DevOps Guru alerts customers to operational issues across Amazon RDS engines. However, it can be complicated and time-consuming to determine the exact cause of a database-related issue because developers often need to enlist database administrators to manually run diagnostic tools and queries to determine the factors contributing to the issue. Once the cause of the issue is identified, database experts often need to do additional analysis to fully understand the problem (e.g. analyze database-specific metrics, events, and wait conditions or extract and analyze relevant SQL statements) before providing guidance on how to fix it. As a result, it can take hours or days to uncover and remediate underlying database issues that put application availability or user experience at risk.

Amazon DevOps Guru for RDS is a new machine learning powered capability in Amazon DevOps Guru that is designed to automatically detect and diagnose performance bottlenecks and operational issues in a database and provide detailed remediation recommendations, enabling developers to resolve issues in minutes rather than days. Amazon DevOps Guru for RDS builds on the capabilities of Amazon DevOps Guru for detecting database-related issues to include additional performance-related issues in Amazon RDS (e.g. resource over-utilization and misbehavior of certain SQL queries). Amazon DevOps Guru for RDS is designed to immediately notify developers when issues occur and provide diagnostic information on the root cause, details on the extent of the problem, and intelligent remediation recommendations to help customers quickly resolve database-related performance bottlenecks and operational issues. For example, if an application performance issue related to an unexpected high load on a database is detected, Amazon DevOps Guru for RDS conducts a root cause analysis to find the exact SQL statement causing the issue, sends a notification with the cause and scope of the issue, and recommends corrective actions to resolve the issue quickly. Amazon DevOps Guru for RDS currently works with Amazon Aurora and is planned to support additional Amazon RDS database engines in 2022. To get started with Amazon DevOps Guru for RDS, visit aws.amazon.com/devops-guru/features/devops-guru-for-rds.

Mercado Libre is a leading technology company in e-commerce in Latin America. “Even though our users may not need to check their past orders frequently, they expect to be able to view past orders, re-order items, and get product information at any time,” said Oscar Mullin, Director of IT – Core Services and Cross SRE and DBA Head at Mercado Libre. “Amazon DynamoDB Standard-IA will provide us with the ability to store our users’ infrequently accessed data at a significant cost savings, while continuing to deliver for our users by maintaining the same high performance, accessibility, and reliability we’ve come to expect from Amazon DynamoDB.”

NetApp is a cloud-led, data-centric software company that gives companies the freedom to put their data to work in the applications that elevate their business. “NetApp offers cloud services to enable organizations to easily run highly efficient, cost-effective relational database migration and operation programs from on premises to the cloud. However, some organizations running applications that require customization to the database environment and operating system have been unable to move to a fully managed database service in the cloud due to the customizations these applications require,” said Ronen Schwartz, SVP and GM at NetApp Cloud Volumes. “With Amazon RDS Custom, these organizations now have a managed database service for applications that require operating system and database customization. Organizations can run Amazon RDS Custom on NetApp ONTAP to benefit from advanced data protection, autonomous efficiencies, and continuous optimizations.”

Amazon Fulfillment Technologies designs, develops, and operates fulfillment technology solutions for Amazon fulfillment centers, including automated Amazon Robotics worldwide. “My team manages a large fleet database. Amazon DevOps Guru for RDS helps us identify a wider range of performance anomalies than our threshold-based monitoring without being overly noisy,” said Brent Bigonger, Principal Database Engineer at Amazon Fulfillment Technologies. “Amazon DevOps Guru for RDS’s machine learning powered insights act as an early warning system that allows us to detect, diagnose, and remediate performance-related issues quickly.”

Jobvite is a recruiting software platform built to attract, hire, and onboard top talent. “Our usage patterns change time to time and as a result, our application interacts with Amazon Aurora databases in ways we can’t always predict. When we see congestion on our AWS databases it can take us hours to figure out the source and remediate,” said Ron Teeter, VP of Engineering and Chief Architect at Jobvite. “We are excited to use Amazon DevOps Guru for RDS to get alerts as soon as an event like this happens. With Amazon DevOps Guru for RDS, we can quickly locate database queries in our application that are causing the performance or operational issues along with an explanation of why it’s happening.”

Singular simplifies marketing data by unifying siloed data, applying attribution, and exposing insights to accelerate growth. “At Singular, we capture, analyze, and refine billions of data points to deliver the most accurate, timely, and actionable cross-platform analytics to our customers. Having immediate access to our data, even if it is infrequently used, is crucial for us to offer our customers the best insights to grow their business fast,” said Ofir Nir, Head of Data Infrastructure at Singular. “The ability to simplify the management and access to our long-term data storage while still benefiting from Amazon DynamoDB performance, durability, and data availability with the Amazon DynamoDB Standard-IA table class could help us further optimize costs and provide an even better user experience to our customers.”

NTT DOCOMO, Inc. is a leading mobile phone operator in Japan. “We manage 45 independent applications for our customers and internal teams at NTT DOCOMO. These teams provide underlying components for public services by NTT DOCOMO and business applications for our company staffers,” said Chikara Mitsui, Senior Manager, Service Design Department at NTT DOCOMO, Inc. “We are excited to use Amazon DevOps Guru for RDS and leverage its machine learning powered insights to quickly detect, diagnose, and remediate a wide range of database-related performance issues. Amazon DevOps Guru provides a single view of insights for our application stack and empowers my team to focus on building more reliable services instead of taking time to investigate operational issues.”

Delphix provides an automated DevOps data platform, masking data for privacy compliance, securing data from ransomware, and delivering efficient, virtualized data for CI/CD and digital transformation. “With Amazon RDS Custom, Delphix customers can accelerate database migrations to a managed service by eliminating data-related bottlenecks that slow down application development velocity,” says Jason Grauel, VP of Product Management at Delphix. “Now, customers can ensure test data keeps pace with an accelerated DevOps cadence while enjoying the operational benefits of Amazon RDS Custom automation.”

Jungle Scout is an all-in-one platform for finding, launching, and selling Amazon products. “Data is the most critical component of our business offering at Jungle Scout. We collect and analyze hundreds of petabytes of data to deliver the most accurate marketplace analytics data in the world to our SMB and Enterprise customers,” said Regan Wolfrom, DevSecOps and Builder Tools manager at Jungle Scout. “The new Amazon DynamoDB Standard-Infrequent Access table class is a massive win for us, allowing us to quickly and efficiently implement cost-effective long-term data storage while still enjoying the benefits of Amazon DynamoDB. The ability to switch between Amazon DynamoDB table classes without any code changes will allow us to easily optimize our costs as we scale and focus our engineering efforts on the features our customers require as they grow their business.”

About Amazon Web Services

For over 15 years, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud offering. AWS has been continually expanding its services to support virtually any cloud workload, and it now has more than 200 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, virtual and augmented reality (VR and AR), media, and application development, deployment, and management from 81 Availability Zones within 25 geographic regions, with announced plans for 27 more Availability Zones and nine more AWS Regions in Australia, Canada, India, Indonesia, Israel, New Zealand, Spain, Switzerland, and the United Arab Emirates. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com.

About Amazon

Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Amazon strives to be Earth’s Most Customer-Centric Company, Earth’s Best Employer, and Earth’s Safest Place to Work. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Career Choice, Fire tablets, Fire TV, Amazon Echo, Alexa, Just Walk Out technology, Amazon Studios, and The Climate Pledge are some of the things pioneered by Amazon. For more information, visit amazon.com/about and follow @AmazonNews.

View source version on businesswire.com: https://www.businesswire.com/news/home/20211201005993/en/